no code implementations • 12 Apr 2023 • Saman Motamed, Jianjin Xu, Chen Henry Wu, Fernando de la Torre
By using ~40 reference images, PATMAT creates anchor points in MAT's style module, and tunes the model using the fixed anchors to adapt the model to a new face identity.
no code implementations • CVPR 2023 • Jinqi Luo, Zhaoning Wang, Chen Henry Wu, Dong Huang, Fernando de la Torre
Extensive experiments demonstrate that our method is capable of producing counterfactual images and offering sensitivity analysis for model diagnosis without the need for a test set.
no code implementations • 23 Mar 2023 • Jinqi Luo, Zhaoning Wang, Chen Henry Wu, Dong Huang, Fernando de la Torre
Rather than relying on a carefully designed test set to assess ML models' failures, fairness, or robustness, this paper proposes Semantic Image Attack (SIA), a method based on the adversarial attack that provides semantic adversarial images to allow model diagnosis, interpretability, and robustness.
2 code implementations • 11 Oct 2022 • Chen Henry Wu, Fernando de la Torre
The commonly-adopted formulation of the latent code of diffusion models is a sequence of gradually denoised samples, as opposed to the simpler (e. g., Gaussian) latent space of GANs, VAEs, and normalizing flows.
1 code implementation • 14 Sep 2022 • Chen Henry Wu, Saman Motamed, Shaunak Srivastava, Fernando de la Torre
Our experiments demonstrate how PromptGen can efficiently sample from several unconditional generative models (e. g., StyleGAN2, StyleNeRF, diffusion autoencoder, NVAE) in a controlled or/and de-biased manner using various off-the-shelf models: (1) with the CLIP model as control, PromptGen can sample images guided by text, (2) with image classifiers as control, PromptGen can de-bias generative models across a set of attributes or attribute combinations, and (3) with inverse graphics models as control, PromptGen can sample images of the same identity in different poses.
1 code implementation • 5 Sep 2022 • Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, Tao Yu
Departing from recent in-context learning methods, we formulate an annotation-efficient, two-step framework: selective annotation that chooses a pool of examples to annotate from unlabeled data in advance, followed by prompt retrieval that retrieves task examples from the annotated pool at test time.
1 code implementation • 16 Jan 2022 • Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on Task-Oriented Dialogue Systems on KVRET
2 code implementations • ACL 2022 • Yusen Zhang, Ansong Ni, Ziming Mao, Chen Henry Wu, Chenguang Zhu, Budhaditya Deb, Ahmed H. Awadallah, Dragomir Radev, Rui Zhang
To the best of our knowledge, Summ$^N$ is the first multi-stage split-then-summarize framework for long input summarization.
1 code implementation • ACL 2022 • Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed H. Awadallah, Dragomir Radev
Transformer-based models have achieved state-of-the-art performance on short-input summarization.
1 code implementation • EMNLP 2021 • Chen Henry Wu, Yinhe Zheng, Xiaoxi Mao, Minlie Huang
Grounded dialogue models generate responses that are grounded on certain concepts.
2 code implementations • 3 Aug 2021 • Hao Zhou, Pei Ke, Zheng Zhang, Yuxian Gu, Yinhe Zheng, Chujie Zheng, Yida Wang, Chen Henry Wu, Hao Sun, Xiaocong Yang, Bosi Wen, Xiaoyan Zhu, Minlie Huang, Jie Tang
Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue data and the model size compared with English ones.
1 code implementation • Findings (ACL) 2021 • Fei Huang, Zikai Chen, Chen Henry Wu, Qihan Guo, Xiaoyan Zhu, Minlie Huang
First, we observe that most words in the transferred sentence can be aligned with related words in the source sentence, so we explicitly model word alignments to suppress irrelevant words.